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Local structure preservation in Kernel space for feature selection
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DOI：10.1007/s11042-018-6926-0.png)
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摘要
En 中文
For many machine learning and data mining tasks in the information explosion environment, one is often confronted with very high dimensional heterogeneous data. Demands for new methods to select discrimination and valuable features that are beneficial to classification and cluster have increased. In this paper, we propose a novel feature selection method to jointly map original data from input space to kernel space and conduct both subspace learning (via locality preserving projection) and feature selection (via a sparsity constraint). Specifically, the nonlinear relationship between data is explored adequately through mapping data from original low-dimensional space to kernel space. Meanwhile, the subspace learning technique is leveraged to preserve available information of local structure in ambient space. Last, by restricting the sparsity of the coefficient matrix, the weight of some features is 0. As a result, we eliminate redundant and irrelevant features and thus make our method select informative and distinguishing features. By comparing our proposed method with some state-of-the-art methods, the experimental results demonstrate that the proposed method outperformed the comparisons in terms of clustering task.
Keyword:
Feature selection
Kernel method
Subspace learning
Sparse learning
Locality preserving projection
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IF:
3
论文数:
2.0W
被引数:
3.2W
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